Course Grade and GPA Prediction

Švábenský et al. (2024) pdf

• Classification models for predicting grades (worse than an average grade, “unsuccessful”, or equal/better than an average grade, “successful”)
• Investigating bias based on university students' regional background in the context of the Philippines
• Demographic groups based on 1 of 5 locations from which students accessed online courses in Canvas
• Bias evaluation using AUC, weighted F1-score, and MADD showed consistent results across all groups, no unfairness was observed

Lee and Kizilcec (2020) pdf

• Models predicting college success (or median grade or above)
• Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian), for male students than female students
• Random forest algorithms performed significantly worse for male students than female students
• The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values
  

Yu et al. (2020) pdf

• Models predicting undergraduate course grades and average GPA

• Students who are international, first-generation, or from low-income households were inaccurately predicted to get lower course grade and average GPA than their peer, and fairness of models improved with the inclusion of clickstream and survey data
• Female students were inaccurately predicted to achieve greater short-term and long-term success than male students, and fairness of models improved when a combination of institutional and click data was used in the model

Riazy et al. (2020) pdf

• Models predicting course outcome of students in a virtual learning environment (VLE)
• More male students were predicted to pass the course than female students, but  this overestimation was fairly small and not consistent across different algorithms
• Among the algorithms, Naive Bayes had the lowest normalized mutual information value and the highest ABROCA value, or differences between the area under curve
• Students with self-declared disability were predicted to pass the course more often

Jiang & Pardos (2021) pdf

• Predicting university course grades using LSTM
• Roughly equal accuracy across racial groups
• Slightly better accuracy (~1%) across racial groups when including race in model

Kung & Yu (2020) pdf

• Predicting course grades and later GPA at public U.S. university
• Five algorithms and three metrics (independence, separation, sufficiency) analyzed
• Poorer performance for Latinx students on course grade prediction for all three metrics; poorer performance for Latinx students on GPA prediction in terms of independence and sufficiency, but not separation
• Poorer performance for first-generation students on course grade prediction for independence and separation, and for some algorithms for GPA prediction as well
• Poorer performance for low-income students in several cases, about 1/3 of cases checked

Jeong et al. (2022) [1]

• Predicting 9th grade math score from academic performance, surveys, and demographic information
• Despite comparable accuracy, model tends to overpredict Asian and White students' performance, and underpredict Black, Hispanic, and Native American students' performance
• Several fairness correction methods equalize false positive and false negative rates across groups.

Sha et al. (2022) [2]

• Predicting course pass/fail with random forest in Open University data
• A range of over-sampling methods tested
• Regardless of over-sampling method used, course pass/fail performance was moderately better for males

Deho et al. (2023) [3]

• Predicting whether course grade will be above or below 0.5
• Better prediction for female students in some courses, better prediction for male students in other courses
• Generally worse prediction for international students